Survey of resource-efficient backbones for computer vision for each domain
Survey of resource-efficient backbones for computer vision for each domain
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
arXiv paper abstract https://arxiv.org/abs/2406.05612
arXiv PDF paper https://arxiv.org/pdf/2406.05612
GitHub https://github.com/pranavphoenix/Backbones
In … computer vision … particularly image classification … remains a gap in understanding the performance of … resource-efficient backbones across … domains and dataset sizes.
… study … evaluates … lightweight, pre-trained CNN backbones under … variety of datasets, including natural images, medical images, galaxy images, and remote sensing images.
… aims to aid … practitioners in selecting the most suitable backbone for their specific problem, especially in … small datasets where fine-tuning a pre-trained network is crucial.
Even though attention-based architectures are gaining popularity, … observed that they tend to perform poorly under low data finetuning tasks compared to CNNs.
… also observed that some CNN architectures such as ConvNeXt, RegNet and EfficientNet performs well compared to others on a diverse set of domains consistently.
… provide … insights into the performance trade-offs and effectiveness of … backbones, facilitating informed decision-making in model … for … spectrum of … domains …
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